|
NeuroCOLT
Technical Report NC-TR-96-008
Dynamic Recurrent
Neural Networks: a Dynamical Analysis
Jean-Philippe
DRAYE and Davor PAVISIC
Faculté Polytechnique de Mons
Belgium
Guy
CHERON and Gaetan LIBERT
University of Brussels
Belgium
Abstract
In this paper, we explore the dynamical features of a neural network
model which presents two types of adaptative parameters: the classical
weights between the units and the time constants associated with each
artificial neuron. The purpose of this study is to provide a strong
theoretical basis for modeling and simulating dynamic recurrent neural
networks. In order to achieve this, we study the effect of the statistical
distribution of the weights and of the time constants on the network
dynamics and we make a sta tistical analysis of the neural transformation.
We examine the network power spectra (to draw some conclusions over
the frequent ial behavior of the network) and we compute the stability
regions to explore the stability of the model. We show that
the network is sensitive to the variations of the mean values of the
weights and the time constants (because of the temporal aspects of
the learned tasks). Nevertheless, our results highlight the improvements
in the network dynamics due to the introduction of adaptative time
constants and indicate that dynamic recurrent neural networks can
bring new powerful features in the field of neural computing.
Download Compressed
Postscript
Title
page
|